Big Data Analytics: what it is and why it matters

What is Big Data Analytics?

Big Data Analytics can be summed up as the process of uncovering patterns, trends, and correlations in large amounts of data (which includes both structured and unstructured data) to help businesses make better, data-driven decisions. In other words, Big Data Analytics processes transform the raw data into a usable resource that a given organisation can use to built better future outcomes thanks to predictive analytics.

How does Big Data Analytics work?

Each Big Data Analytics process data in 4 distinctive steps:

Step 1: Data Collection

The way of collecting necessary data differs between organisations and depends on the available tools. Nevertheless, with today’s technologies, both structured data and the raw unstructured “mess” from various sources (e.g., from a data warehouse, data lake, cloud storage and so on) can be gathered and used for business analytics.

Step 2: Data Processing

After the data gets collected and safely stored, it’s time to get it organised – that’s the only way to get viable results of analytical queries. Especially when you have to process data that had no structure or came in massive amounts. One of the most commonly used options is batch processing, which enables you to process large blocks of data over time. This method is especially useful, when the turnaround time between collection and analysis is long. If time is of the essence, stream processing would do better – it handles data in smaller batches, ensuring shorter turnaround time and quick decision-making. Unfortunately, due to being more complex, this type of data processing is often significantly more expensive.

Step 3: Data Cleaning

For stronger results, the data quality should be improved. This means that all the gathered and processed data must be saved in a correct format and any duplicates/irrelevant data must be accounted for or eliminated. Leaving “dirty” data in would lead to flawed, obscured insights and misleading conclusions.

Step 4: Data Analysis

With nice and organised data sets, the proper data science can begin – it’s time to turn all this big data into even bigger insights and data models through advanced analytics. Exact methods depend on the nature of the data and set business goals.

Thanks to the software and hardware advancements of the last 20 plus years, it’s possible to handle tremendous amounts of data and thoroughly explore the data surrounding each active business. Moreover, this field is constantly evolving and providing us with new, even more effective big data analytics tools. Speaking of tools…

What technologies does Big Data Analytics include?

As mentioned above, various types of tools can be used in big data analysis. Here are some technologies most commonly used in BDA:

• Data mining – this technology can be used for discovering patterns even in copious amounts of raw and unstructured data, which in turn can be further analysed and provide answers for complex business questions. With this type of software tools, you can pinpoint all the relevant data and use it to predict most likely outcomes, thus shortening the process of making decisions based on useful info.
• Text mining – this allows you to analyse data in text form. This data can be taken from books, the web, advertisements, comment fields or practically any other text-based sources, and can provide various insights you could’ve missed. Text mining technology is based on natural language processing or machine learning.
• Machine learning – in short, it’s a specific type of artificial intelligence (AI) that “trains” machines how to learn from provided data. With this, a machine can produce automatic and quick data analysing models, even on a large scale. These models can get very precise and provide an organisation with a better chance of avoiding risks and picking the most profitable business opportunities.
• Data storage – safe storing of structured and unstructured data is a crucial part of big data analytics – it provides both protection from outside influence and easy access for data analysts and business users. Raw data can be stored in so-called data lakes, while structured data can be put in data warehouses – both methods are complementary and can be used jointly.
• Cloud computing – it’s a scalable, subscription-based solution that removes many financial and physical barriers for IT – all the analytical computing power is located elsewhere, and you don’t have to invest in expensive IT equipment. This service can grow with your business, so you won’t have to worry about computing power when your organisation gets bigger.
• Data management – with the constant flow of new data, establishing and maintaining standards for data quality is a must. Thus, managing big data requires a solid program that can ensure reliable analytics and keep the whole enterprise on the same level regarding data governing.
• Predictive analytics – this technology uses historical data, machine-learning techniques, and statistical algorithms to predict the most likely outcomes. It can be used for e.g., marketing campaigns, assessing risks, choosing the most promising operations or even fraud detection.
• In-memory analytics – with this, your data can be analysed directly in your company’s system memory (instead of your computer’s hard drive), which removes prep time and analytical processing latencies. Thanks to this technology, you can get meaningful insights almost immediately and stay agile with your decision-making processes. As a plus, it can provide both iterative and interactive test scenarios.
• Hadoop – a very popular, open-source software framework that stores and processes big data using commodity hardware clusters. Thanks to this data distribution, it can store and process vast amounts of varied, complex data, and run parallel applications. The data processing is fast, which makes this tool essential when dealing with large volumes of data.

Why should you use Big Data Analytics?

Now that you know what it is, and what it can include, let’s take a look at how big data analytics benefits your business. The benefits of using BDA can be divided into 3 main areas:

• Cost reduction – with big data technologies at your side, you can greatly reduce various costs tied to running a successful business. For example, cloud-based solutions can drastically lower the costs tied to hardware (buying, maintaining, upgrading etc.) and storing vast amounts of data. Plus, BDA can provide you with more cost-efficient ways of conducting business, based on market trends, consumer data, and other viable data sources.
• Faster and better decision-making – key technologies of BDA, such as in-memory analytics and streaming data through IoT, enable fast analyses and provide you with on-demand info that help you make better decisions based on relevant, solid data.
• Easier development and marketing of new products/services – thanks to the data collected by your organisation and its analyses, you can easily gauge customer satisfaction and assess their needs. In short, processing such data enables you to give them exactly what they want, when they want. This makes it way easier to introduce new, profitable products/services and adjust your business actions to the ever-changing market trends.

Why is Big Data Analytics important? – summary

Data generated by an organisation and its surroundings (the so-called Big Data) is a valuable resource that can provide a serious business boost when processed properly (this is especially true for unstructured big data). Thus, big data analytics refers to various technologies that enable you to transform all that stored data into a usable source of information. When conducted properly, BDA can help you make better business decisions and carry your organisation into a bright future. If you’re in need of big data analytics services, don’t hesitate to contact us – here at Silicon Cities Consulting & Advisory, we can help you with various data-related problems and provide expert advisory services (e.g., board advisory) to your company.